IIARD International Institute of Academic Research and Development

WORLD JOURNAL OF INNOVATION AND MODERN TECHNOLOGY (WJIMT )

E-ISSN 2504-4766
P-ISSN 2682-5910
VOL. 8. NO. 2 2024
DOI: 10.56201/wjimt.v8.no2.2024.pg71.89

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Cyber Security Techniques for Detecting and Preventing Cross-Site Scripting Attacks

Okusi, Oluwatobiloba

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Abstract

As the demand as well as use of web application increases daily so also different cyber security threats to it increases alarmingly. Cross-Site Scripting (XSS) attack is one in which an attacker exploits website and web application vulnerabilities by injecting arbitrary web requests into a web page viewed by other users or store unauthorized cookies, thereby attacking and causing harms to the owners’ sites and accounts. This study explores XSS attacks, and proposes some cyber security techniques for detecting and preventing XSS. The results of the analysis show that the proposed Deep Forest (DF) model alongside some other AI techniques can help address the issue of class imbalance, an aspect of XSS neglected by extant studies. In conclusion, the study contributes to solving some critical issues of cyber security. Researchers are charged to take up more studies on XSS, with specific focus on DF and class imbalance in cyber security, towards addressing more national and international cyber security threats and effects.

keywords:

Cyber security, Cross-site scripting, Techniques, Detecting, Preventing

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